Blockchain–Cloud Integration for Cross-Border Invoice Factoring: A Secure and Scalable Framework for Sustainable Trade Finance
Bibliographic record
Abstract
Cross-border trade finance supports liquidity and global supply chain growth, especially for small and medium enterprises (SMEs). Traditional documentation and invoice factoring systems face delays, huge documentation, fraud risks, poor interoperability, and limited transparency which results in unsustainable action. Blockchain-based trade finance improves immutability, decentralized validation, and automation through smart contracts. However, it suffers from scalability limitations, fragmented data management, and regulatory integration issues. Academic literature lacks a unified framework combining blockchain and cloud to address these operational gaps. This study fills the gap by evaluating traditional, blockchain, and hybrid models in trade finance systems. The paper reviews major use cases to understand realworld blockchain implementations in trade finance. These include HSBC and ING's blockchain letter of credit, and Bank of Canada's Project Jasper. UBS's blockchain payment initiative is also analyzed for its real-time settlement and auditability benefits. The study compares traditional and blockchain systems across speed, security, transparency, and integration parameters. It identifies that blockchain alone is insufficient due to its lack of scalability and limited system compatibility. To address this, the paper proposes a Blockchain-Cloud Integrated Trade Finance (TF) Framework. This includes hybrid data storage, Application Programming Interface (API) compliance, digital identity verification, and stakeholder dashboards. Using a design science approach, the framework improves auditability, transparency, and regulatory alignment. Findings benefit regulators, banks, and FinTech's. The framework not only enhances efficiency and compliance but also contributes to sustainable trade finance
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".